Determination of the concentration range for 267 proteins from 21 lots of commercial human plasma using highly multiplexed multiple reaction monitoring mass spectrometry
Bibliographic record
Abstract
Multiple reaction monitoring (MRM) is a key tool for biomarker validation and the translation of potential biomarkers into the clinic. To demonstrate the applicability of MRM towards achieving this goal, we set out to determine the concentration ranges of 267 plasma proteins, including 61 FDA-approved/LDT developed biomarkers, in 21 commercial human plasma lots, as well as to assess accuracy and precision. Each target protein was quantified by calculating the area ratio of the endogenous tryptic target peptide to its stable isotope-labelled internal standard equivalent and compared to a standard curve. This highly multiplexed approach utilized a standard-flow UHPLC system linked to a triple quadrupole. All samples were analyzed across three separate days and assessed for robustness and accuracy. The standard curves and quality control samples showed excellent performance, with >93% of standards and QCs meeting the acceptance criteria. A total of 248 proteins were able to be quantified in at least one sample on at least one of the three days, with 111 proteins being quantified in all 21 samples on all three days. The protein concentrations across all proteins covered six orders of magnitude. Furthermore, excellent three-day precision was demonstrated with 86% of CVs falling below 15%. Overall, the protein concentration differences ranged from 1.1-fold for metalloproteinase inhibitor 2, to 69-fold for serum amyloid A-1/A-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".